Paper summarynipsreviewsThe paper addresses the problem of compressive sensing MRI (CS-MRI) by proposing a "deep unfolding" approach (cf. http://arxiv.org/abs/1409.2574) with a sparsity-based data prior and inference via ADMM. All layers of the proposed ADMM-Net are based on a generalization of ADMM inference steps and are discriminatively trained to minimize a reconstruction error. In contrast to other methods for CS-MRI, the proposed approach offers both high reconstruction quality and fast run-time.
The basic idea is to convert the convention optimization based CS reconstruction algorithm into a fixed neural network learned with back-propagation algorithm. Specifically, the ADMM-based CS reconstruction is approximated with a deep neural network. Experimental results show that the approximated neural network outperforms several existing CS-MRI algorithms with less computational time.
The ADMM algorithm has proven to be useful for solving problems with differentiable and non-differentiable terms, and therefore has a clear link with compressed sensing. Experiments prove some gain in performance with respect to the state of the art, specially in terms of computational cost at test time.

The paper addresses the problem of compressive sensing MRI (CS-MRI) by proposing a "deep unfolding" approach (cf. http://arxiv.org/abs/1409.2574) with a sparsity-based data prior and inference via ADMM. All layers of the proposed ADMM-Net are based on a generalization of ADMM inference steps and are discriminatively trained to minimize a reconstruction error. In contrast to other methods for CS-MRI, the proposed approach offers both high reconstruction quality and fast run-time.
The basic idea is to convert the convention optimization based CS reconstruction algorithm into a fixed neural network learned with back-propagation algorithm. Specifically, the ADMM-based CS reconstruction is approximated with a deep neural network. Experimental results show that the approximated neural network outperforms several existing CS-MRI algorithms with less computational time.
The ADMM algorithm has proven to be useful for solving problems with differentiable and non-differentiable terms, and therefore has a clear link with compressed sensing. Experiments prove some gain in performance with respect to the state of the art, specially in terms of computational cost at test time.